Shrikanth Narayanan

About

Shrikanth (Shri) Narayanan is a University Professor and holder of the Niki and Max Nikias Chair in Engineering at the University of Southern California (USC). He serves as the inaugural Vice President for Presidential Initiatives on the Senior Leadership Team of USC's President. He is a Professor in the Signal and Image Processing Institute of USC's Ming Hsieh Electrical & Computer Engineering department with joint appointments in Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head and Neck Surgery. Shri Narayanan is also the inaugural director of the Ming Hsieh Institute, a Research Director for the Information Sciences Institute at USC, and a Visiting Faculty Researcher at Google. His academic background includes a Master's degree, Engineer's degree, and Ph.D. in electrical engineering from UCLA, and a Bachelor's degree from Anna University in India. His research focuses on human-centered sensing, signal processing, and machine intelligence related to human communication, emotions, and behavior, with applications in defense, security, health, media, and the arts. He has published extensively, holds numerous patents, and has contributed to technology commercialization through startups he co-founded, such as Behavioral Signals Technologies and Lyssn.

Research topics

• Computer Science
• Artificial Intelligence
• Speech recognition
• Psychology
• Medicine
• Machine Learning
• Natural Language Processing
• World Wide Web
• Mathematics
• Biology

Selected publications

• Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

  Journal of Counseling Psychology · 2020 · 130 citations

  Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects…

  DOI

• Clinical state tracking in serious mental illness through computational analysis of speech

  PLoS ONE · 2020 · 73 citations

  Senior authorCorresponding

  Individuals with serious mental illness experience changes in their clinical states over time that are difficult to assess and that result in increased disease burden and care utilization. It is not known if features derived from speech can serve as a transdiagnostic marker of these clinical states. This study evaluates the feasibility of collecting speech samples from people with serious mental illness and explores the potential utility for tracking changes in clinical state over time. Patients…

  DOI

• Vocal tract shaping of emotional speech

  Computer Speech & Language · 2020 · 29 citations

  Senior authorCorresponding
  PublisherOA PDFDOI

• Improved 3D real‐time MRI of speech production

  Magnetic Resonance in Medicine · 2021 · 22 citations

  PURPOSE: To provide 3D real-time MRI of speech production with improved spatio-temporal sharpness using randomized, variable-density, stack-of-spiral sampling combined with a 3D spatio-temporally constrained reconstruction. METHODS: temporal order. The strategy yielding highest image quality was chosen as the proposed method. We evaluated the proposed and original 3D real-time MRI methods in 2 healthy subjects performing speech production tasks that invoke rapid movements of articulators seen in…

  DOI

• Complexity of vocal tract shaping in glossectomy patients and typical speakers: A principal component analysis

  The Journal of the Acoustical Society of America · 2021 · 16 citations

  Senior authorCorresponding

  The glossectomy procedure, involving surgical resection of cancerous lingual tissue, has long been observed to affect speech production. This study aims to quantitatively index and compare complexity of vocal tract shaping due to lingual movement in individuals who have undergone glossectomy and typical speakers using real-time magnetic resonance imaging data and Principal Component Analysis. The data reveal that (i) the type of glossectomy undergone largely predicts the patterns in vocal tract…

  DOI

Recent grants

• Dynamics of Vocal Tract Shaping

  NIH · $416k · 2005–2009

• Collaborative Research: Modeling Creative and Emotive Improvisation in Theater Performance

  NSF · $422k · 2008–2012

• Dynamics of Vocal Tract Shaping

  NIH · $5.7M · 2005–2021

Frequent coauthors

• Athina P. Petropulu

  Rutgers, The State University of New Jersey

  488 shared

• Tülay Adalı

  University of Maryland, Baltimore County

  488 shared

• Ahmed H. Tewfik

  Apple (United Kingdom)

  488 shared

• Sergios Theodoridis

  National and Kapodistrian University of Athens

  486 shared

• Fernando Pereira

  426 shared

• R Baseil

  Indian Institute of Science Bangalore

  356 shared

• Vice Presdient

  The University of Texas at Austin

  320 shared

• K.V.S. Hari

  Indian Institute of Science Bangalore

  292 shared

Education

• Ph.D., Electrical Engineering

  University of California, Los Angeles

  1989

• M.S., Electrical Engineering

  University of California, Los Angeles

  1984

• B.S., Electrical Engineering

  Indian Institute of Technology, Madras

  1982

Awards & honors

• IEEE James L. Flanagan Speech and Audio Processing Award (20…
• Edward J. McCluskey Technical Achievement Award from the IEE…
• ISCA Medal for Scientific Achievement (2023)
• IEEE SPS Claude Shannon-Harry Nyquist Technical Achievement…
• ACM ICMI Sustained Accomplishment Award (2020)

Similar researchers at University of Southern California

• Andreas Molischh-132
• Demetrios Christodoulidesh-113
• Bhaskar Krishnamacharih-82